Comparative life cycle assessment of on-site sanitation systems using lagoons or drying beds for fecal sludge treatment in low-income tropical countries
Bibliographic record
Abstract
ABSTRACT Environmental challenges in low-income countries, such as Haiti, persist due to inadequate sanitation infrastructure. This study assesses the environmental impacts of nine on-site sanitation systems to identify those with the least environmental impacts and explore improvement options. Nine scenarios were developed, each representing different systems for managing 1 ton of fecal sludge over 1 year. The ‘Impact World + ’ and ‘IPCC 2013 GWP 100a’ methods evaluated impacts on ecosystems, human health, and climate change. Data sources included interviews, weighing records, and scientific publications. Results show that Scenario 8 (Flush Toilet – Evacuation – Planted Drying Beds) is most impactful on health (1.17 × 10−2 DALY), while Scenario 1 (Composting Toilet – Evacuation – Unplanted Drying Beds) is least impactful (1.77 × 10−3 DALY). For ecosystem impacts, Scenario 2 (Container-based Toilet – Evacuation – Planted Drying Beds) is most impactful (3.81 × 103 PDF·m2·year), while Scenario 6 (VIP latrine – Evacuation – Lagoons) is least impactful (3.52 × 103 PDF·m2·year). Key hotspots include toilet paper, wood shavings, GHG emissions, and water use. The study recommends an integrated approach combining environmental life cycle assessment (LCA) with life cycle cost assessment and social LCA for sustainable decision-making on sanitation systems in low-income countries.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".